sklearn-docs/anomaly-detection

Model description

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Intended uses & limitations

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Training Procedure

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Hyperparameters

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Hyperparameter	Value
memory
steps	[('scaler', StandardScaler()), ('model', IsolationForest(max_samples=100, random_state=0))]
verbose	False
scaler	StandardScaler()
model	IsolationForest(max_samples=100, random_state=0)
scaler__copy	True
scaler__with_mean	True
scaler__with_std	True
model__bootstrap	False
model__contamination	auto
model__max_features	1.0
model__max_samples	100
model__n_estimators	100
model__n_jobs
model__random_state	0
model__verbose	0
model__warm_start	False

Model Plot

Pipeline(steps=[('scaler', StandardScaler()),('model', IsolationForest(max_samples=100, random_state=0))])

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Evaluation Results

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How to Get Started with the Model

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Model Card Authors

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Citation

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Decision Boundary

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